Imagining Probabilistic Belief Change as Imaging (Technical Report)
This work addresses theoretical challenges in probabilistic reasoning for AI and logic communities, but it appears incremental as it builds upon existing imaging concepts.
The paper tackles the problem of probabilistic belief change by proposing Expected Distance Imaging (EDI), a new framework that generalizes Bayesian conditioning and existing imaging methods for both revision and update, with analysis of weight functions and four specific instantiations.
Imaging is a form of probabilistic belief change which could be employed for both revision and update. In this paper, we propose a new framework for probabilistic belief change based on imaging, called Expected Distance Imaging (EDI). EDI is sufficiently general to define Bayesian conditioning and other forms of imaging previously defined in the literature. We argue that, and investigate how, EDI can be used for both revision and update. EDI's definition depends crucially on a weight function whose properties are studied and whose effect on belief change operations is analysed. Finally, four EDI instantiations are proposed, two for revision and two for update, and probabilistic rationality postulates are suggested for their analysis.